A Systematic Review of Apps using Mobile Criteria for Adolescent Pregnancy Prevention (mCAPP)
Bibliographic record
Abstract
BACKGROUND: Adolescents in the United States and globally represent a high-risk population for unintended pregnancy, which leads to high social, economic, and health costs. Access to smartphone apps is rapidly increasing among youth, but little is known about the strategies that apps employ to prevent pregnancy among adolescents and young adults. Further, there are no guidelines on best practices for adolescent and young adult pregnancy prevention through mobile apps. OBJECTIVE: This review developed a preliminary evaluation framework for the assessment of mobile apps for adolescent and young adult pregnancy prevention and used this framework to assess available apps in the Apple App Store and Google Play that targeted adolescents and young adults with family planning and pregnancy prevention support. METHODS: We developed an assessment rubric called Mobile Criteria for Adolescent Pregnancy Prevention (mCAPP) for data extraction using evidence-based and promising best practices from the literature. mCAPP comprises 4 domains: (1) app characteristics, (2) user interface features, (3) adolescent pregnancy prevention best practices, and (4) general sexual and reproductive health (SRH) features. For inclusion in the review, apps that advertised pregnancy prevention services and explicitly mentioned youth, were in English, and were free were systematically identified in the Apple App Store and Google Play in 2015. Screening, data extraction, and 4 interrater reliability checks were conducted by 2 reviewers. Each app was assessed for 92 facets of the mCAPP checklist. RESULTS: Our search returned 4043 app descriptions in the Apple App Store (462) and Google Play (3581). After screening for inclusion criteria, 22 unique apps were included in our analysis. Included apps targeted teens in primarily developed countries, and the most common user interface features were clinic and health service locators. While app strengths included provision of SRH education, description of modern contraceptives, and some use of evidence-based adolescent best practices, gaps remain in the implementation of the majority of adolescent best practices and user interface features. Of the 8 best practices for teen pregnancy prevention operationalized through mCAPP, the most commonly implemented best practice was the provision of information on how to use contraceptives to prevent pregnancy (15/22), followed by provision of accurate information on pregnancy risk of sexual behaviors (13/22); information on SRH communication, negotiation, or refusal skills (10/22); and the use of persuasive language around contraceptive use (9/22). CONCLUSIONS: The quality and scope of apps for adolescent pregnancy prevention varies, indicating that developers and researchers may need a supportive framework. mCAPP can help researchers and developers consider mobile-relevant evidence-based best practices for adolescent SRH as they develop teen pregnancy prevention apps. Given the novelty of the mobile approach, further research is needed on the impact of mCAPP criteria via mobile channels on adolescent health knowledge, behaviors, and outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".